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http://appris.bioinfo.cnio.es/

A database that houses annotations of human splice isoforms. It adds reliable protein structural and functional data and information from cross-species conservation. A visual representation of the annotations for each gene allows users to easily identify functional changes brought about by splicing events. In addition to collecting, integrating and analyzing reliable predictions of the effect of splicing events, it also selects a single reference sequence for each gene, termed the principal isoform, based on the annotations of structure, function and conservation for each transcript.

Proper citation: APPRIS (RRID:SCR_012019) Copy   

•   Source: SciCrunch Registry

http://jbirc.jbic.or.jp/h-dbas/

A specialized database for human alternative splicing (AS) based on H-Invitational full-length cDNAs. H-DBAS offers unique data and viewer for human Alternative Splicing (AS) analysis. It contains: * Genome-wide representative alternative splicing variants (RASVs) identified from following datasets * H-Inv full-length cDNAs (resource summary): H-Invitational cDNA dataset * H-Inv all transcripts (resource summary): Published human mRNA dataset * Mouse full-length cDNAs (resource summary): Mouse cDNA dataset * RASVs affecting protein functions such as protein motif, GO, subcellular localization signal and transmembrane domain * Conserved RASVs compared with mouse genome and the full-length cDNAs (H-Inv full-length cDNAs only)

Proper citation: Human Transcriptome Database for Alternative Splicing (RRID:SCR_013305) Copy   

•   Source: SciCrunch Registry

http://dorina.mdc-berlin.de/rbp_browser/dorina.html

In animals, RNA binding proteins (RBPs) and microRNAs (miRNAs) post-transcriptionally regulate the expression of virtually all genes by binding to RNA. Recent advances in experimental and computational methods facilitate transcriptome-wide mapping of these interactions. It is thought that the combinatorial action of RBPs and miRNAs on target mRNAs form a post-transcriptional regulatory code. We provide a database that supports the quest for deciphering this regulatory code. Within doRiNA, we are systematically curating, storing and integrating binding site data for RBPs and miRNAs. Users are free to take a target (mRNA) or regulator (RBP and/or miRNA) centric view on the data. We have implemented a database framework with short query response times for complex searches (e.g. asking for all targets of a particular combination of regulators). All search results can be browsed, inspected and analyzed in conjunction with a huge selection of other genome-wide data, because our database is directly linked to a local copy of the UCSC genome browser. At the time of writing, doRiNA encompasses RBP data for the human, mouse and worm genomes. For computational miRNA target site predictions, we provide an update of PicTar predictions.

Proper citation: doRiNA (RRID:SCR_013222) Copy   

•   Source: SciCrunch Registry

http://agem.cnb.csic.es/VisualOmics/aGEM/

Database platform of an integrated view of eight databases (mouse gene expression resources: EMAGE, GXD, GENSAT, BioGPS, ABA, EUREXPRESS; human gene expression databases: HUDSEN, BioGPS and Human Protein Atlas) that allows the experimentalist to retrieve relevant statistical information relating gene expression, anatomical structure (space) and developmental stage (time). Moreover, general biological information from databases such as KEGG, OMIM and MTB is integrated too. It can be queried using gene and anatomical structure. Output information is presented in a friendly format, allowing the user to display expression maps and correlation matrices for a gene or structure during development. An in-depth study of a specific developmental stage is also possible using heatmaps that relate gene expression with anatomical components. This is a powerful tool in the gene expression field that makes easy the access to information related to the anatomical pattern of gene expression in human and mouse, so that it can complement many functional genomics studies. The platform allows the integration of gene expression data with spatial-temporal anatomic data by means of an intuitive and user friendly display., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: aGEM (RRID:SCR_013349) Copy   

•   Source: SciCrunch Registry

http://www.phenogo.org

PhenoGO is a computed database designed for high throughput mining that provides phenotypic and experimental context - such as the cell type, disease, tissue, and organ - to existing annotations between gene products and Gene Ontology (GO) terms, as specified in the Gene Ontology Annotations (GOA) for multiple model organisms. Phenotypic and Experimental (P&E) contexts to identifiers are computationally mapped to general biological ontologies, including: the Cell Ontology (CO), phenotypes from the Unified Medical Language System (UMLS), species from Taxonomy of the National Center for Biotechnology Information (NCBI) taxonomy, and specialized ontologies such as Mammalian Phenotype Ontology (MP) and Mouse Anatomy (MA).

Proper citation: PhenoGO (RRID:SCR_013646) Copy   

•   Source: SciCrunch Registry

http://servers.binf.ku.dk/bloodspot/

Database that provides gene expression profiles of genes and gene signatures in healthy and malignant hematopoiesis and includes data from both humans and mice. In addition to the default plot, which displays an integrated expression plot, two additional levels of visualization are available: an interactive tree showing the hierarchical relationship between the samples, and a Kaplan-Meier survival plot. The database is sub-divided into several datasets that are accessible for browsing.

Proper citation: BloodSpot (RRID:SCR_015563) Copy   

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http://compartments.jensenlab.org/Downloads

Web resource that integrates evidence on protein subcellular localization from manually curated literature, high-throughput screens, automatic text mining, and sequence-based prediction methods. All evidence is mapped to common protein identifiers and Gene Ontology terms, and further unify it by assigning confidence scores that facilitate comparison of the different types and sources of evidence and visualize these scores on a schematic cell.

Proper citation: COMPARTMENTS Subcellular localization database (RRID:SCR_015561) Copy   

•   Source: SciCrunch Registry

http://mirgator.kobic.re.kr/

Database of compiled, public, deep sequencing miRNA data and several novel tools to facilitate exploration of massive data. The miR-seq browser supports users to examine short read alignment with the secondary structure and read count information available in concurrent windows. Features such as sequence editing, sorting, ordering, import and export of user data are of great utility for studying iso-miRs, miRNA editing and modifications. miRNA����??target relation is essential for understanding miRNA function. Coexpression analysis of miRNA and target mRNAs, based on miRNA-seq and RNA-seq data from the same sample, is visualized in the heat-map and network views where users can investigate the inverse correlation of gene expression and target relations, compiled from various databases of predicted and validated targets.

Proper citation: miRGator (RRID:SCR_007793) Copy   

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http://polya.umdnj.edu/

A database of mRNA polyadenylation sites. PolyA_DB version 1 contains human and mouse poly(A) sites that are mapped by cDNA/EST sequences. PolyA_DB version 2 contains poly(A) sites in human, mouse, rat, chicken and zebrafish that are mapped by cDNA/EST and Trace sequences. Sequence alignments between orthologous sites are available. PolyA_SVM predicts poly(A) sites using 15 cis elements identified for human poly(A) sites.

Proper citation: PolyA DB (RRID:SCR_007867) Copy   

•   Source: SciCrunch Registry

http://www.chibi.ubc.ca/Gemma

Resource for reuse, sharing and meta-analysis of expression profiling data. Database and set of tools for meta analysis, reuse and sharing of genomics data. Targeted at analysis of gene expression profiles. Users can search, access and visualize coexpression and differential expression results.

Proper citation: Gemma (RRID:SCR_008007) Copy   

•   Source: SciCrunch Registry

http://bmbpcu36.leeds.ac.uk/RE1db_mkII/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 15, 2013. A database containing all genomic human and mouse binding sites of the Repressor Element 1 Silencing Transcription factor (REST), identified by PSSM. The RE1 silencing transcription factor (REST; also known as the neuron-restrictive silencer factor), is a nine zinc-finger transcription factor, related to the Gli-Kruppel family. REST binds to a conserved 21-nucleotide element, known as repressor element 1 (RE1; also known as the neuron-restrictive silencer element). REST was proposed to be a ''master'' silencer of neuron specific gene expression in non-neuronal tissues and undifferentiated neuroepithelium (precursor of neuronal cells), preventing the default expression of the neuronal phenotype during embryogenesis. It has been shown to function independently of orientation and distance from a gene promoter. REST has an important role during embryonic development, as homozygous gene knockout mice (Rest-/-) die by embryonic day 11.5. The constitutive expression of REST has also been shown to disrupt neuronal gene expression and cause axon path finding errors in chicken embryos (Paquette et al. 2000). RE1 sequences that are known to bind REST have also been found near to non-neuronal genes, including keratin and cytochrome P450 genes.

Proper citation: Neuron-Restrictive Silencer Factor (RRID:SCR_008546) Copy   

•   Source: SciCrunch Registry

http://dnatraffic.ibb.waw.pl/

DNAtraffic database is dedicated to be an unique comprehensive and richly annotated database of genome dynamics during the cell life. DNAtraffic contains extensive data on the nomenclature, ontology, structure and function of proteins related to control of the DNA integrity mechanisms such as chromatin remodeling, DNA repair and damage response pathways from eight model organisms commonly used in the DNA-related study: Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Saccharomyces cerevisiae, Schizosaccharomyces pombe, Escherichia coli and Arabidopsis thaliana. DNAtraffic contains comprehensive information on diseases related to the assembled human proteins. Database is richly annotated in the systemic information on the nomenclature, chemistry and structure of the DNA damage and drugs targeting nucleic acids and/or proteins involved in the maintenance of genome stability. One of the DNAtraffic database aim is to create the first platform of the combinatorial complexity of DNA metabolism pathway analysis. Database includes illustrations of pathway, damage, protein and drug. Since DNAtraffic is designed to cover a broad spectrum of scientific disciplines it has to be extensively linked to numerous external data sources. Database represents the result of the manual annotation work aimed at making the DNAtraffic database much more useful for a wide range of systems biology applications. DNAtraffic database is freely available and can be queried by the name of DNA network process, DNA damage, protein, disease, and drug.

Proper citation: DNAtraffic (RRID:SCR_008886) Copy   

•   Source: SciCrunch Registry

https://scicrunch.org/scicrunch/data/source/nlx_154697-3/search?q=*

A virtual database currently indexing available cell lines from: Coriell Cell Repositories, International Mouse Strain Resource (IMSR), ATCC, NIH Human Pluripotent Stem Cell Registry, NIGMS Human Genetic Cell Repository, and Developmental Therapeutics Program.

Proper citation: Integrated Cell Lines (RRID:SCR_008994) Copy   

•   Source: SciCrunch Registry

https://scicrunch.org/kravitz2

Dataset of the spike and laser timestamps from Kravitz, Owen and Kretizer's 2012 paper "Optogenetic identification of striatal projection neuron subtypes during in vivo recordings." The code will analyze spike trains around laser pulses to determine if a cell is significantly activated by the laser, and therefore expresses an excitatory opsin, such as channelrhodopsin-2. It returns an excel sheet that simply identifies the activated cells.

Proper citation: Kravitz Dataset 2 (RRID:SCR_000296) Copy   

•   Source: SciCrunch Registry

http://neomorph.salk.edu/brain_methylomes/

THIS RESOURCE IS NO LONGER IN SERVICE. Datasets described in the manuscript: "Global Epigenomic Reconfiguration During Mammalian Brain Development" (Science, 2013 - DOI: 10.1126/science.1237905. This study provides genome-wide composition, patterning, cell specificity, and dynamics of DNA methylation at single-base resolution in human and mouse frontal cortex throughout their lifespan. Widespread methylome reconfiguration occurs during fetal to young adult development, coincident with synaptogenesis.

Proper citation: Mammalian Brain Methylomes (RRID:SCR_001648) Copy   

•   Source: SciCrunch Registry

http://www.neuro.mpg.de/connectomics

Data set of the dense reconstruction of 950 neurons and their mutual contacts for the mouse inner plexiform layer--the main computational neuropil region in the mammalian retina. This was achieved by applying a combination of crowd-sourced manual annotation and machine-learning-based volume segmentation to serial block-face electron microscopy data. They characterize a new type of retinal bipolar interneuron and show that they can subdivide a known type based on connectivity. Circuit motifs that emerge from their data indicate a functional mechanism for a known cellular response in a ganglion cell that detects localized motion, and predict that another ganglion cell is motion sensitive. A Data browser is also available for download

Proper citation: Connectomic reconstruction of the inner plexiform layer in the mouse retina (RRID:SCR_002246) Copy   

•   Source: SciCrunch Registry

http://bpg.utoledo.edu/~afedorov/lab/eid.html

Data sets of protein-coding intron-containing genes that contain gene information from humans, mice, rats, and other eukaryotes, as well as genes from species whose genomes have not been completely sequenced. This is a comprehensive and convenient dataset of sequences for computational biologists who study exon-intron gene structures and pre-mRNA splicing. The database is derived from GenBank release 112, and it contains protein-coding genes that harbor introns, along with extensive descriptions of each gene and its DNA and protein sequences, as well as splice motif information. They have created subdatabases of genes whose intron positions have been experimentally determined. The collection also contains data on untranslated regions of gene sequences and intron-less genes. For species with entirely sequenced genomes, species-specific databases have been generated. A novel Mammalian Orthologous Intron Database (MOID) has been introduced which includes the full set of introns that come from orthologous genes that have the same positions relative to the reading frames.

Proper citation: EID: Exon-Intron Database (RRID:SCR_002469) Copy   

•   Source: SciCrunch Registry

http://www.thebiogrid.org/

Curated protein-protein and genetic interaction repository of raw protein and genetic interactions from major model organism species, with data compiled through comprehensive curation efforts.

Proper citation: Biological General Repository for Interaction Datasets (BioGRID) (RRID:SCR_007393) Copy   

•   Source: SciCrunch Registry

http://www.gensat.org/daily_showcase.jsp

THIS RESOURCE IS NO LONGER IN SERVICE, documented on March 19, 2012. Due to budgetary constraints, the National Center for Biotechnology Information (NCBI) has discontinued support for the NCBI GENSAT database, and it has been removed from the Entrez System. The Gene Expression Nervous System Atlas (GENSAT) project involves the large-scale creation of transgenic mouse lines expressing green fluorescent protein (GFP) reporter or Cre recombinase under control of the BAC promoter in specific neural and glial cell populations. BAC expression data for all the lines generated (over 1300 lines) are available in online, searchable databases (www.gensat.org and the Database of GENSAT BAC-Cre driver lines). If you have any specific questions, please feel free to contact us at info_at_ncbi.nlm.nih.gov The GENSAT project aims to map the expression of genes in the central nervous system of the mouse, using both in situ hybridization and transgenic mouse techniques. Search criteria include gene names, gene symbols, gene aliases and synonyms, mouse ages, and imaging protocols. Mouse ages are restricted to E10.5 (embryonic day 10.5), E15.5 (embryonic day 15.5), P7 (postnatal day 7), and Adult (adult). The project focuses on two techniques * Evaluation of unmodified mice lines for expression of a given gene using radiolabelled riboprobes and in-situ hybridization. * Creation of transgenic mice lines containing a BAC construct that expresses a marker gene in the same environment as the native gene

Proper citation: GENSAT at NCBI - Gene Expression Nervous System Atlas (RRID:SCR_003923) Copy   

•   Source: SciCrunch Registry

http://genomequebec.mcgill.ca/PReMod

Database that describes more than 100,000 computational predicted transcriptional regulatory modules within the human genome. These modules represent the regulatory potential for 229 transcription factors families and are the first genome-wide / transcription factor-wide collection of predicted regulatory modules for the human genome. The algorithm used involves two steps: (i) Identification and scoring of putative transcription factor binding sites using 481 TRANSFAC 7.2 position weight matrices (PWMs) for vertebrate transcription factors. To this end, each non-coding position of the human genome was evaluated for its similarity to each PWM using a log-likelihood ratio score with a local GC-parameterized third-order Markov background model. Corresponding orthologous positions in mouse and rat genomes were evaluated similarly and a weighted average of the human, mouse, and rat log-likelihood scores at aligned positions (based on a Multiz (Blanchette et al. 2004) genome-wide alignment of these three species) was used to define the matrix score for each genomic position and each PWM. (ii) Detection of clustered putative binding sites. To assign a module score to a given region, the five transcription factors with the highest total scoring hits are identified, and a p-value is assigned to the total score observed of the top 1, 2, 3, 4, or 5 factors. The p-value computation takes into consideration the number of factors involved (1 to 5), their total binding site scores, and the length and GC content of the region under evaluation. Users can retrieve all information for a given region, a given PWM, a given gene and so on. Several options are given for textual output or visualization of the data.

Proper citation: PReMod (RRID:SCR_003403) Copy   

•   Source: SciCrunch Registry